Clinical evaluation of targeted arterial perfusion of verapamil and chemotherapeutic drugs in interventional therapy of advanced lung cancer
Bibliographic record
Abstract
PURPOSE: To assess the clinical efficacy of targeted arterial perfusion of verapamil and chemotherapeutic agents in the interventional therapy of lung cancer. METHODS: Forty patients with advanced lung cancer underwent treatment with targeted arterial perfusion of verapamil and chemotherapeutic agents using Seldinger technique. Interventional therapy was performed once a month, and each subject received interventional treatment for 2 or more cycles. The therapeutic efficacy was evaluated 2 months post-treatment. RESULTS: Out of 40 patients with advanced lung cancer, 5 cases achieved complete remission (CR) and 29 cases achieved partial remission (PR), with a total effectiveness (CR + PR) rate of 85 %. Besides, 32 cases achieved significantly alleviated clinical symptoms, and 29 cases had decreased clinical tumor stage. All subjects had stable karnofsky performance status score and body weight. Among the 40 patients, 13 cases had leucopenia, 10 cases had gastrointestinal reactions, 3 cases presented with elevated alanine aminotransferase/aspartate aminotransferase ratio, and 3 cases had fever. However, all these side effects relieved quickly. No elevation of BUN/Cr ratio and allergic reactions was observed. No significant changes in cardiac function and electrocardiogram were noticed after the treatment. CONCLUSIONS: Targeted arterial perfusion of verapamil and chemotherapeutic drugs can improve the clinical symptoms of patients with advanced lung cancer and increase the efficacy of chemotherapeutic agents, thereby providing an opportunity for radiotherapy or surgical treatment for advanced lung cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".